doarima.m

ARIMA模型

% script to do an arima(p,d,q) analysis; you  must put the name of your datafile
% (for example suppose it is mydata.dat) in the statement dataname='mydata.dat'
%
% note there will be other scripts to remove polynomials
% or to do SARIMA

ALPHA=.01; % Pr[type 1 error] for threshold line to test acf for N(0,1/sqrt(n)) 

pvec=[0,1,2]; % orders of AR
%pvec=[4,5,6];
d=0; % number of differences
qvec=[0,1,2]; % orders of MA
%qvec=[2,3];
RESIDPLOTS=1; % > 0 to plot residual acfs

%dataname='data2(:,1)'; % assumed loaded
%descriptor='US Production'; % strings to be used for plotting
%xvalues='quarters';
%dindex=[1:120];	% select which of the input data to use
brillx=textread('d:\brillx.txt','%f'); % this puts 912 elements in a column
brilly=textread('d:\brilly.txt','%f');
dataname='brillx';     %%%%%%%%%%%%% dataname statement %%%%%%%%%%%%%%%%%%%
descriptor='WOBBLE';
xvalues='time';
dindex=[1:200];
dindex=[1:length(brillx)];



eval(['data=',dataname,';']); % puts the input into data

ddata=data(dindex);             % difference it d times
for j=1:d
   ddata=diff(ddata);
end
% ddata has been 1st differenced d times
nd=length(ddata);

%%%%%%%%%% now plot ddiff and acf
figure;
subplot(211);
plot([1:nd],ddata,'-');
title(['Result of ',int2str(d),' 1st differences of data: ',descriptor]);

subplot(212); % now the acf
rhod=xcov(ddata,'coeff'); % this returns the acf normalized by the variance
% it is even, so ignore the negative lags and take only half of the positive ones
rhod=rhod(nd:nd+floor(nd/2)); % floor needed in case nd/2 not integer
nrho=length(rhod);
stem(rhod,'.');	% a stem plot with dot marker
hold on;
plot(zeros(1,nrho));
thr=norminv(1-ALPHA/2,0,1/sqrt(nd));
plot([1:nrho],thr*ones(1,nrho),'--r',[1:nrho],-thr*ones(1,nrho),'--r');
title(['autocorrelation, n=',int2str(nd),' alpha = ',num2str(ALPHA)]);

% now estimate ARMA model; ARMAX is AX=BU + Ce so identify phi with A and theta with C
np=length(pvec);
nq=length(qvec);
aicsave=-99*ones(np,nq);
fpesave=-99*ones(np,nq);
minaic=1e+6;
for pp=1:np 
    p=pvec(pp);
    for qq=1:nq
        q=qvec(qq);
        if p+q ~=0
            orders=[p q];
            m=armax(ddata,orders);      % m is a structure with the model stuff in it
            resids=pe(m,ddata);         % pe returns the prediction errors
            nres=length(resids);
            rhores=xcov(resids,'coeff'); % this returns the acf normalized by the variance
            % it is even, so ignore the negative lags and take only half of the positive ones
          
            rhores=rhores(nres:nres+floor(nres/2)); % floor needed in case nd/2 not integer
            nrho=length(rhores);                    % now rhores(1) is zero lag
            % next compute the Ljung - Box statistic and P-values
            deltak=floor(nrho/10)+1;
            kvec=[p+q+1:deltak:p+q+1+4*deltak];
            for kk=1:5
                Q=0;
                for j=2:kvec(kk)+1
                    Q=Q+(rhores(j).^2)/(nd-j);
                end
                Q=nd*(nd-1)*Q;
                ljpv(kk)=1-chi2cdf(Q,kvec(kk)-p-q);  % df=kvec(kk)-p-q
            end
            aicsave(pp,qq)=aic(m);
            fpesave(pp,qq)=fpe(m);
            if aicsave(pp,qq) < minaic
                minaic=aicsave(pp,qq); % save the min
                pbest=p;
                qbest=q;
                mbest=m;
            end
            disp(sprintf('(p,d,q)=(%d,%d,%d) aic=%d  fpe=%d',p,d,q,aicsave(pp,qq),fpesave(pp,qq)));
            QQ=[kvec;ljpv];
            disp(sprintf('Ljung-Box P-values : '));
            disp(sprintf('  K=%d P-v=%6.3e \n',QQ(:)));
            
            if RESIDPLOTS
            %%%%%%%%%% resids and acf
            figure;
            subplot(211);
            plot([1:nd],resids,'-');
            title(['Residuals from ARIMA(',sprintf('%d,%d,%d)',p,d,q),' for data: ',descriptor]);

            subplot(212); % now the acf
     
            stem(rhores,'.');	% a stem plot with dot marker
            hold on;
            plot(zeros(1,nrho));
            thr=norminv(1-ALPHA/2,0,1/sqrt(nres));
            plot([1:nrho],thr*ones(1,nrho),'--r',[1:nrho],-thr*ones(1,nrho),'--r');
            title(['autocorrelation of residuals, n=',int2str(nres),' alpha = ',num2str(ALPHA)]);
            end % RESIDPLOTS

        end
    end
end
disp(['SUMMARY OF ARIMA MODELS FOR DATA : ',descriptor]);
disp(sprintf('AIC min at (p,d,q)=(%d,%d,%d) aic=%d',pbest,d,qbest,aic(mbest)));
[A,B,C,D,F]=polydata(mbest);
disp(['\phi coefficients : ',num2str(A)]);
disp(['\theta coefficients : ',num2str(C)]);



% now plot periodogram and estimated spectra from model

%%%%%%%%%%%%%%  plot |W(\lambda)|^2
NLAM=100;	% no. pts to plot in  [0,2\pi]
dlam=pi/NLAM;
lams=[0:dlam:pi-dlam];
clear i;
e=exp(-i*lams);

thetaspec=polyval(C,e);
phispec=polyval(A,e);
sigr=std(resids);

W=thetaspec./phispec;
%%% xt=newpgram(x,nfft,np1,np2,halflen,alpha,rejalpha,plsw,logsw,dataname)
%estspec=pgram(ddata,nd,[],0,.01,descriptor);
%estspec=newpgram(ddata',NLAM*2,1,NLAM,8,.01,.001,0,0,descriptor);
%estspec=estspec(1:floor(nd/2));

estspec=avspec(ddata',NLAM*2,NLAM,0); % 
modspec=(sigr^2)/(2*pi)*abs(W).^2;

figure;
semilogy(lams,estspec,'r',lams,modspec,'b-.');
xlabel(' 0 \leq \lambda < \pi');
v=axis;
text(v(1)+.2*(v(2)-v(1)),v(3)+.8*(v(4)-v(3)),['theta:',...
      sprintf(' %4.2f',C),sprintf('\n phi:'),sprintf(' %4.2f',A)]);
title('spectral density estimates - log scale');
legend('periodogram','estimated model');